Spam Detection Using Logistic Regression

This project builds a spam detection model using Logistic Regression to classify text messages as spam or ham. The workflow includes data cleaning, text preprocessing, TF-IDF feature extraction, model training, and evaluation using standard machine learning metrics.

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📌 Project Summary

• Uses a labeled SMS dataset (spam.csv)
• Converts text into numerical vectors using TF-IDF
• Trains a Logistic Regression classifier with scikit-learn
• Evaluates performance using accuracy and classification metrics
• Fully implemented in a Jupyter Notebook

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⚙️ Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Jupyter Notebook

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📁 Dataset

The dataset spam.csv contains two main columns:

• text — message content
• label — spam or ham

Ensure spam.csv is located in the project folder.

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🚀 How to Run

Install dependencies:

pip install scikit-learn pandas numpy